{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T05:25:56Z","timestamp":1730265956288,"version":"3.28.0"},"reference-count":23,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"DOI":"10.1109\/ijcnn48605.2020.9206597","type":"proceedings-article","created":{"date-parts":[[2020,9,30]],"date-time":"2020-09-30T00:40:33Z","timestamp":1601426433000},"page":"1-8","source":"Crossref","is-referenced-by-count":1,"title":["Generalising Recursive Neural Models by Tensor Decomposition"],"prefix":"10.1109","author":[{"given":"Daniele","family":"Castellana","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Davide","family":"Bacciu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1996.548916"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-4013"},{"key":"ref13","first-page":"442","article-title":"Tensorizing neural networks","author":"novikov","year":"2015","journal-title":"Proceedings of NIPS 2015 - Volume 1 NIPS&#x2019;15"},{"key":"ref14","article-title":"Automatic differentiation in PyTorch","author":"paszke","year":"2017","journal-title":"Proc NIPS Autodiff Workshop"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(90)90005-K"},{"key":"ref16","first-page":"1631","article-title":"Recursive deep models for semantic compositionality over a sentiment treebank (RNTN)","author":"socher","year":"2013","journal-title":"Proceedings of EMNLP 2013"},{"key":"ref17","article-title":"Labeling RAAM","author":"sperduti","year":"1994","journal-title":"Technical report Connection Science"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/72.572108"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1150"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.285"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2012.2228226"},{"key":"ref6","article-title":"Tucker tensor layer in fully connected neural networks","volume":"abs 1903 6133","author":"calvi","year":"2019","journal-title":"CoRR"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W15-4002"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1137\/S0895479896305696"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8851851"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2012.2222044"},{"key":"ref1","article-title":"Learning Tree Distributions by Hidden Markov Models","author":"bacciu","year":"2018","journal-title":"Workshop on Learning and Automata (LearnAut&#x2019;18)"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/72.712151"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/BF02289464"},{"key":"ref22","article-title":"Event representations with tensor-based compositions","author":"weber","year":"0","journal-title":"AAAI 2017"},{"key":"ref21","article-title":"Deep graph library: Towards efficient and scalable deep learning on graphs","author":"wang","year":"2019","journal-title":"ICLR Workshop on Representation Learning on Graphs and Manifolds"},{"key":"ref23","article-title":"ADADELTA: an adaptive learning rate method","author":"zeiler","year":"2012","journal-title":"arXiv preprint arXiv 1212 5701"}],"event":{"name":"2020 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2020,7,19]]},"location":"Glasgow, United Kingdom","end":{"date-parts":[[2020,7,24]]}},"container-title":["2020 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9200848\/9206590\/09206597.pdf?arnumber=9206597","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T21:58:34Z","timestamp":1656453514000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9206597\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/ijcnn48605.2020.9206597","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}